This clinical trial studies a new screening program to improve the early detection of sporadic pancreatic cancer in individuals with a high risk of developing pancreatic cancer. Pancreatic cancer remains one of the deadliest solid tumors, characterized by a long phase without symptoms followed by rapid progression once clinically evident. Despite advancements in treatment, the survival rate for pancreatic cancer remains low. Research has helped to identify a subset of individuals with a markedly high short-term risk for developing pancreatic cancer, which includes adults aged 50 and older with glycemically-defined new-onset diabetes and an Enriching New-Onset Diabetes for Pancreatic Cancer (ENDPAC) score ≥ 3. However, current practice guidelines do not provide clear pathways for surveillance or early detection. The screening program in this trial combines repeated contrast-enhanced computed tomography (CT) scans using artificial intelligence (AI) and blood draws. Contrast-enhanced CT is an imaging technique which creates a series of detailed pictures of areas inside the body; the pictures are created by a computer linked to an x-ray machine and a contrast agent is used to enhance the images. The images are then reviewed using AI, which may make it easier to spot cancer earlier on the CT scans than with the human eye. Studying samples of blood in the laboratory from high-risk individuals may help doctors understand more about why they may develop pancreatic cancer. This may be an effective way to screen high-risk individuals and improve the early detection of sporadic pancreatic cancer.
Study sponsor and potential other locations can be found on ClinicalTrials.gov for NCT07324096.
Locations matching your search criteria
United States
Minnesota
Rochester
Mayo Clinic in RochesterStatus: Approved
Contact: Ajit H Goenka
Phone: 507-284-2511
PRIMARY OBJECTIVE:
I. To evaluate the feasibility and potential clinical utility of serial AI-augmented computed tomography (CT) imaging and longitudinal blood biobanking in individuals with glycemically-defined new-onset diabetes (gNOD) and elevated pancreatic cancer risk (ENDPAC score ≥ 3), with the aim of determining whether this approach enables earlier detection of pancreatic ductal adenocarcinoma (PDA) relative to symptom-driven diagnosis.
SECONDARY OBJECTIVES:
I. To determine the proportion of PDA cases diagnosed at potentially curable stages (stage 0/I) in the interventional cohort compared to the observational cohort.
II. To assess adherence and retention feasibility for serial imaging and biobanking protocols over a 12-month period in a high-risk, asymptomatic population.
III. To evaluate the rate and clinical implications of false-positive findings from AI-augmented imaging compared to radiologist interpretation, including downstream diagnostic burden and follow-up procedures.
IV. To assess whether cases identified by AI exhibit a shorter time to diagnosis compared to those identified through routine radiology reports, using head-to-head, blinded comparisons.
V. To establish feasibility benchmarks for future multi-institutional implementation, including participant recruitment yield, imaging protocol adherence, biospecimen collection rates, and data integration using semi-automated or automated informatics platforms for high-risk identification and outcome tracking.
TERTIARY OBJECTIVE:
I. To create a longitudinal, clinically annotated biorepository of blood products from a high-risk gNOD cohort to enable future discovery and validation of circulating biomarkers for early pancreatic ductal adenocarcinoma (PDAC) detection.
OUTLINE: Patients self-select between 1 of 3 groups.
GROUP A1: Patients undergo contrast-enhanced abdominal CT and blood sample collection at baseline, 6 months, and 12 months in the absence of unacceptable toxicity. Patients also undergo electronic medical record (EMR) surveillance for PDA diagnosis for up to 36 months.
GROUP A2: Patients undergo blood sample collection at baseline, 6 months, and 12 months in the absence of unacceptable toxicity. Patients also undergo EMR surveillance for PDA diagnosis for up to 36 months.
GROUP B: Patients undergo EMR surveillance for PDA diagnosis for up to 36 months.
Trial PhaseNo phase specified
Trial Typediagnostic
Lead OrganizationMayo Clinic in Rochester
Principal InvestigatorAjit H Goenka
